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<a href="#nested-classes">类</a> &#124;
<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pri-types">Private 类型</a> &#124;
<a href="#pri-methods">Private 成员函数</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
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<div class="title">pcl::rec_3d_framework::LocalRecognitionPipeline&lt; Distance, PointInT, FeatureT &gt; 模板类 参考</div>  </div>
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<p><a class="el" href="class_object.html">Object</a> recognition + 6DOF pose based on local features, GC and HV Contains keypoints/local features computation, matching using FLANN, point-to-point correspondence grouping, pose refinement and hypotheses verification Available features: SHOT, FPFH See apps/3d_rec_framework/tools/apps for usage  
 <a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="local__recognizer_8h_source.html">local_recognizer.h</a>&gt;</code></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
类</h2></td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1flann__model.html">flann_model</a></td></tr>
<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">class &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1_object_hypothesis.html">ObjectHypothesis</a></td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:a1371e59928d0d5d71307431e2bfd0fed"><td class="memItemLeft" align="right" valign="top"><a id="a1371e59928d0d5d71307431e2bfd0fed"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><b>setISPK</b> (typename <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; FeatureT &gt;::Ptr &amp;signatures, PointInTPtr &amp;p, PointInTPtr &amp;keypoints)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setVoxelSizeICP</b> (float s)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setSearchModel</b> (std::string &amp;id)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setThresholdAcceptHyp</b> (float t)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setKdtreeSplits</b> (int n)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setIndices</b> (std::vector&lt; int &gt; &amp;indices)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setICPIterations</b> (int it)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setUseCache</b> (bool u)</td></tr>
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boost::shared_ptr&lt; std::vector&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html">ModelT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getModels</b> ()</td></tr>
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boost::shared_ptr&lt; std::vector&lt; Eigen::Matrix4f, Eigen::aligned_allocator&lt; Eigen::Matrix4f &gt; &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getTransforms</b> ()</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a66e5b925348f24f654c0eec71a4f50b0">setDataSource</a> (typename boost::shared_ptr&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_source.html">Source</a>&lt; PointInT &gt; &gt; &amp;source)</td></tr>
<tr class="memdesc:a66e5b925348f24f654c0eec71a4f50b0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the model data source_ <br /></td></tr>
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boost::shared_ptr&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_source.html">Source</a>&lt; PointInT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>getDataSource</b> ()</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#ad27e491f241b233e60a640e457eda67c">setFeatureEstimator</a> (typename boost::shared_ptr&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_estimator.html">LocalEstimator</a>&lt; PointInT, FeatureT &gt; &gt; &amp;feat)</td></tr>
<tr class="memdesc:ad27e491f241b233e60a640e457eda67c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the local feature estimator <br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#af0e7e2c8953c779f6f1496647d0633c4">setCGAlgorithm</a> (typename boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_correspondence_grouping.html">CorrespondenceGrouping</a>&lt; PointInT, PointInT &gt; &gt; &amp;alg)</td></tr>
<tr class="memdesc:af0e7e2c8953c779f6f1496647d0633c4"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the CG algorithm <br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#abb02ec116a04534cbc1b8ff21ec2d622">setHVAlgorithm</a> (typename boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_hypothesis_verification.html">HypothesisVerification</a>&lt; PointInT, PointInT &gt; &gt; &amp;alg)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3c1bbb801e20592b87aad086ccb4af65">setInputCloud</a> (const PointInTPtr &amp;cloud)</td></tr>
<tr class="memdesc:a3c1bbb801e20592b87aad086ccb4af65"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the input cloud to be classified <br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a6ebfd3dc168e800da0fea55516ba5c67">setDescriptorName</a> (std::string &amp;name)</td></tr>
<tr class="memdesc:a6ebfd3dc168e800da0fea55516ba5c67"><td class="mdescLeft">&#160;</td><td class="mdescRight">Sets the descriptor name <br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a75df765445fdd31c2737ec6f8f527e90">setTrainingDir</a> (std::string &amp;dir)</td></tr>
<tr class="memdesc:a75df765445fdd31c2737ec6f8f527e90"><td class="mdescLeft">&#160;</td><td class="mdescRight">Filesystem dir where to keep the generated training data <br /></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>setComputeTablePlane</b> (bool b)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>getProcessed</b> (PointInTPtr &amp;cloud)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#aba0aa81d2c10717820508bb806de47c7">initialize</a> (bool force_retrain=false)</td></tr>
<tr class="memdesc:aba0aa81d2c10717820508bb806de47c7"><td class="mdescLeft">&#160;</td><td class="mdescRight">Initializes the FLANN structure from the provided source It does training for the models that havent been trained yet <br /></td></tr>
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<tr class="memitem:a3b869b8212bc0e4643312b1242b72916"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3b869b8212bc0e4643312b1242b72916">recognize</a> ()</td></tr>
<tr class="memdesc:a3b869b8212bc0e4643312b1242b72916"><td class="mdescLeft">&#160;</td><td class="mdescRight">Performs recognition and pose estimation on the input cloud  <a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3b869b8212bc0e4643312b1242b72916">更多...</a><br /></td></tr>
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Private 类型</h2></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointInTPtr</b></td></tr>
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typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>ConstPointInTPtr</b></td></tr>
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typedef Distance&lt; float &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>DistT</b></td></tr>
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typedef <a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html">Model</a>&lt; PointInT &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>ModelT</b></td></tr>
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Private 成员函数</h2></td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>loadFeaturesAndCreateFLANN</b> ()</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>convertToFLANN</b> (const std::vector&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1flann__model.html">flann_model</a> &gt; &amp;models, <a class="el" href="classflann_1_1_matrix.html">flann::Matrix</a>&lt; float &gt; &amp;data)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>nearestKSearch</b> (<a class="el" href="classflann_1_1_index.html">flann::Index</a>&lt; DistT &gt; *index, const <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1flann__model.html">flann_model</a> &amp;model, int k, <a class="el" href="classflann_1_1_matrix.html">flann::Matrix</a>&lt; int &gt; &amp;indices, <a class="el" href="classflann_1_1_matrix.html">flann::Matrix</a>&lt; float &gt; &amp;distances)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>getPose</b> (<a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html">ModelT</a> &amp;model, int view_id, Eigen::Matrix4f &amp;pose_matrix)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>getKeypoints</b> (<a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html">ModelT</a> &amp;model, int view_id, typename <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;::Ptr &amp;keypoints_cloud)</td></tr>
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void&#160;</td><td class="memItemRight" valign="bottom"><b>drawCorrespondences</b> (PointInTPtr &amp;cloud, <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1_object_hypothesis.html">ObjectHypothesis</a> &amp;oh, PointInTPtr &amp;keypoints_pointcloud, pcl::Correspondences &amp;correspondences)</td></tr>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
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std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3b5ec58eb55f43c5b7dc18b051286e72">training_dir_</a></td></tr>
<tr class="memdesc:a3b5ec58eb55f43c5b7dc18b051286e72"><td class="mdescLeft">&#160;</td><td class="mdescRight">Directory where the trained structure will be saved <br /></td></tr>
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PointInTPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a7c4309d4d9e564900e9f230e3c4fdf04">input_</a></td></tr>
<tr class="memdesc:a7c4309d4d9e564900e9f230e3c4fdf04"><td class="mdescLeft">&#160;</td><td class="mdescRight">Point cloud to be classified <br /></td></tr>
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boost::shared_ptr&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_source.html">Source</a>&lt; PointInT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a570b642caf7da50b71ce536fa65a51a6">source_</a></td></tr>
<tr class="memdesc:a570b642caf7da50b71ce536fa65a51a6"><td class="mdescLeft">&#160;</td><td class="mdescRight"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html" title="Model representation">Model</a> data source <br /></td></tr>
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boost::shared_ptr&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_estimator.html">LocalEstimator</a>&lt; PointInT, FeatureT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#abc0861c5e2a7866c3e5d32c8b068ad82">estimator_</a></td></tr>
<tr class="memdesc:abc0861c5e2a7866c3e5d32c8b068ad82"><td class="mdescLeft">&#160;</td><td class="mdescRight">Computes a feature <br /></td></tr>
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boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_correspondence_grouping.html">CorrespondenceGrouping</a>&lt; PointInT, PointInT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">cg_algorithm_</a></td></tr>
<tr class="memdesc:a3f3bf44d5aaaeda4c328e4d58ccf44c1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Point-to-point correspondence grouping algorithm <br /></td></tr>
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boost::shared_ptr&lt; <a class="el" href="classpcl_1_1_hypothesis_verification.html">HypothesisVerification</a>&lt; PointInT, PointInT &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a></td></tr>
<tr class="memdesc:a33fc233406168aa31c6d762457129ec5"><td class="mdescLeft">&#160;</td><td class="mdescRight">Hypotheses verification algorithm <br /></td></tr>
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std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#aa59dee9258bf2617b05e00d14e57c791">descr_name_</a></td></tr>
<tr class="memdesc:aa59dee9258bf2617b05e00d14e57c791"><td class="mdescLeft">&#160;</td><td class="mdescRight">Descriptor name <br /></td></tr>
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std::string&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a8ec7a3935fb1dcf5b884fb80cc457b54">search_model_</a></td></tr>
<tr class="memdesc:a8ec7a3935fb1dcf5b884fb80cc457b54"><td class="mdescLeft">&#160;</td><td class="mdescRight">Id of the model to be used <br /></td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>compute_table_plane_</b></td></tr>
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<a class="el" href="classflann_1_1_matrix.html">flann::Matrix</a>&lt; float &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>flann_data_</b></td></tr>
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<a class="el" href="classflann_1_1_index.html">flann::Index</a>&lt; DistT &gt; *&#160;</td><td class="memItemRight" valign="bottom"><b>flann_index_</b></td></tr>
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std::vector&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_1_1flann__model.html">flann_model</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>flann_models_</b></td></tr>
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std::vector&lt; int &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>indices_</b></td></tr>
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bool&#160;</td><td class="memItemRight" valign="bottom"><b>use_cache_</b></td></tr>
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std::map&lt; std::pair&lt; std::string, int &gt;, typename <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; PointInT &gt;::Ptr &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>keypoints_cache_</b></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><b>threshold_accept_model_hypothesis_</b></td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><b>ICP_iterations_</b></td></tr>
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boost::shared_ptr&lt; std::vector&lt; <a class="el" href="classpcl_1_1rec__3d__framework_1_1_model.html">ModelT</a> &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>models_</b></td></tr>
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boost::shared_ptr&lt; std::vector&lt; Eigen::Matrix4f, Eigen::aligned_allocator&lt; Eigen::Matrix4f &gt; &gt; &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>transforms_</b></td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><b>kdtree_splits_</b></td></tr>
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float&#160;</td><td class="memItemRight" valign="bottom"><b>VOXEL_SIZE_ICP_</b></td></tr>
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PointInTPtr&#160;</td><td class="memItemRight" valign="bottom"><b>keypoints_input_</b></td></tr>
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PointInTPtr&#160;</td><td class="memItemRight" valign="bottom"><b>processed_</b></td></tr>
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<a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; FeatureT &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>signatures_</b></td></tr>
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</table>
<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;template&lt; class &gt; class Distance, typename PointInT, typename FeatureT&gt;<br />
class pcl::rec_3d_framework::LocalRecognitionPipeline&lt; Distance, PointInT, FeatureT &gt;</h3>

<p><a class="el" href="class_object.html">Object</a> recognition + 6DOF pose based on local features, GC and HV Contains keypoints/local features computation, matching using FLANN, point-to-point correspondence grouping, pose refinement and hypotheses verification Available features: SHOT, FPFH See apps/3d_rec_framework/tools/apps for usage </p>
<dl class="section author"><dt>作者</dt><dd>Aitor Aldoma, Federico Tombari </dd></dl>
</div><h2 class="groupheader">成员函数说明</h2>
<a id="a3b869b8212bc0e4643312b1242b72916"></a>
<h2 class="memtitle"><span class="permalink"><a href="#a3b869b8212bc0e4643312b1242b72916">&#9670;&nbsp;</a></span>recognize()</h2>

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<div class="memtemplate">
template&lt;template&lt; class &gt; class Distance, typename PointInT , typename FeatureT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html">pcl::rec_3d_framework::LocalRecognitionPipeline</a>&lt; Distance, PointInT, FeatureT &gt;::recognize</td>
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<p>Performs recognition and pose estimation on the input cloud </p>
<p>POSE REFINEMENT</p>
<p>HYPOTHESES VERIFICATION</p>
<div class="fragment"><div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  {</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;    models_.reset (<span class="keyword">new</span> std::vector&lt;ModelT&gt;);</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;    transforms_.reset (<span class="keyword">new</span> std::vector&lt;Eigen::Matrix4f, Eigen::aligned_allocator&lt;Eigen::Matrix4f&gt; &gt;);</div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160; </div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;    PointInTPtr processed;</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;    <span class="keyword">typename</span> pcl::PointCloud&lt;FeatureT&gt;::Ptr signatures (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;FeatureT&gt;</a> ());</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;    <span class="comment">//pcl::PointCloud&lt;int&gt; keypoints_input;</span></div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;    PointInTPtr keypoints_pointcloud;</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160; </div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;    <span class="keywordflow">if</span> (signatures_ != 0 &amp;&amp; processed_ != 0 &amp;&amp; (signatures_-&gt;size () == keypoints_pointcloud-&gt;points.size ()))</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;    {</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      keypoints_pointcloud = keypoints_input_;</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;      signatures = signatures_;</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;      processed = processed_;</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;      std::cout &lt;&lt; <span class="stringliteral">&quot;Using the ISPK ...&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;    }</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160;    {</div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;      processed.reset( (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>));</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;      <span class="keywordflow">if</span> (indices_.size () &gt; 0)</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;      {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;        PointInTPtr sub_input (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;        <a class="code" href="group__common.html#gaa65b1c8d782e7b776ae682679d2d948f">pcl::copyPointCloud</a> (*<a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a7c4309d4d9e564900e9f230e3c4fdf04">input_</a>, indices_, *sub_input);</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#abc0861c5e2a7866c3e5d32c8b068ad82">estimator_</a>-&gt;estimate (sub_input, processed, keypoints_pointcloud, signatures);</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;      }</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;      <span class="keywordflow">else</span></div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      {</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#abc0861c5e2a7866c3e5d32c8b068ad82">estimator_</a>-&gt;estimate (<a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a7c4309d4d9e564900e9f230e3c4fdf04">input_</a>, processed, keypoints_pointcloud, signatures);</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      }</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;      processed_ = processed;</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160; </div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;    }</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160; </div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;    std::cout &lt;&lt; <span class="stringliteral">&quot;Number of keypoints:&quot;</span> &lt;&lt; keypoints_pointcloud-&gt;points.size () &lt;&lt; std::endl;</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160; </div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;    <span class="keywordtype">int</span> size_feat = <span class="keyword">sizeof</span>(signatures-&gt;points[0].histogram) / <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;    <span class="comment">//feature matching and object hypotheses</span></div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;    std::map&lt;std::string, ObjectHypothesis&gt; object_hypotheses;</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;    {</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> idx = 0; idx &lt; signatures-&gt;points.size (); idx++)</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;      {</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;        <span class="keywordtype">float</span>* hist = signatures-&gt;points[idx].histogram;</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;        std::vector&lt;float&gt; std_hist (hist, hist + size_feat);</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;        flann_model histogram;</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;        histogram.descr = std_hist;</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;        <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;int&gt;</a> indices;</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;        <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;float&gt;</a> distances;</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;        nearestKSearch (flann_index_, histogram, 1, indices, distances);</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;        <span class="comment">//read view pose and keypoint coordinates, transform keypoint coordinates to model coordinates</span></div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;        Eigen::Matrix4f homMatrixPose;</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;        getPose (flann_models_.at (indices[0][0]).model, flann_models_.at (indices[0][0]).view_id, homMatrixPose);</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160; </div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;        <span class="keyword">typename</span> pcl::PointCloud&lt;PointInT&gt;::Ptr keypoints (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a> ());</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;        getKeypoints (flann_models_.at (indices[0][0]).model, flann_models_.at (indices[0][0]).view_id, keypoints);</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160; </div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;        PointInT view_keypoint = keypoints-&gt;points[flann_models_.at (indices[0][0]).keypoint_id];</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;        PointInT model_keypoint;</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        model_keypoint.getVector4fMap () = homMatrixPose.inverse () * view_keypoint.getVector4fMap ();</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160; </div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        <span class="keyword">typename</span> std::map&lt;std::string, ObjectHypothesis&gt;::iterator it_map;</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;        <span class="keywordflow">if</span> ((it_map = object_hypotheses.find (flann_models_.at (indices[0][0]).model.id_)) != object_hypotheses.end ())</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;        {</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;          <span class="comment">//if the object hypothesis already exists, then add information</span></div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;          ObjectHypothesis oh = (*it_map).second;</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;          oh.correspondences_pointcloud-&gt;points.push_back (model_keypoint);</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;          oh.correspondences_to_inputcloud-&gt;push_back (</div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;                                                       <a class="code" href="structpcl_1_1_correspondence.html">pcl::Correspondence</a> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (oh.correspondences_pointcloud-&gt;points.size () - 1),</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;                                                                            <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (idx), distances[0][0]));</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;          oh.feature_distances_-&gt;push_back (distances[0][0]);</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160; </div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;        }</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;        <span class="keywordflow">else</span></div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;        {</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;          <span class="comment">//create object hypothesis</span></div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;          ObjectHypothesis oh;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;          <span class="keyword">typename</span> pcl::PointCloud&lt;PointInT&gt;::Ptr correspondences_pointcloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a> ());</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;          correspondences_pointcloud-&gt;points.push_back (model_keypoint);</div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160; </div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;          oh.model_ = flann_models_.at (indices[0][0]).model;</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;          oh.correspondences_pointcloud = correspondences_pointcloud;</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;          <span class="comment">//last keypoint for this model is a correspondence the current scene keypoint</span></div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160; </div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;          pcl::CorrespondencesPtr corr (<span class="keyword">new</span> pcl::Correspondences ());</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;          oh.correspondences_to_inputcloud = corr;</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;          oh.correspondences_to_inputcloud-&gt;push_back (<a class="code" href="structpcl_1_1_correspondence.html">pcl::Correspondence</a> (0, <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (idx), distances[0][0]));</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160; </div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;          boost::shared_ptr &lt; std::vector&lt;float&gt; &gt; feat_dist (<span class="keyword">new</span> std::vector&lt;float&gt;);</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;          feat_dist-&gt;push_back (distances[0][0]);</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160; </div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;          oh.feature_distances_ = feat_dist;</div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;          object_hypotheses[oh.model_.id_] = oh;</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;        }</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;      }</div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;    }</div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160; </div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;    <span class="keyword">typename</span> std::map&lt;std::string, ObjectHypothesis&gt;::iterator it_map;</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160; </div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;    std::vector&lt;float&gt; feature_distance_avg;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160; </div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;    {</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;      <span class="comment">//pcl::ScopeTime t(&quot;Geometric verification, RANSAC and transform estimation&quot;);</span></div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;      <span class="keywordflow">for</span> (it_map = object_hypotheses.begin (); it_map != object_hypotheses.end (); it_map++)</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;      {</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;        std::vector &lt; pcl::Correspondences &gt; corresp_clusters;</div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">cg_algorithm_</a>-&gt;setSceneCloud (keypoints_pointcloud);</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">cg_algorithm_</a>-&gt;setInputCloud ((*it_map).second.correspondences_pointcloud);</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">cg_algorithm_</a>-&gt;setModelSceneCorrespondences ((*it_map).second.correspondences_to_inputcloud);</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;        <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">cg_algorithm_</a>-&gt;cluster (corresp_clusters);</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160; </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;Instances:&quot;</span> &lt;&lt; corresp_clusters.size () &lt;&lt; <span class="stringliteral">&quot; Total correspondences:&quot;</span> &lt;&lt; (*it_map).second.correspondences_to_inputcloud-&gt;size () &lt;&lt; <span class="stringliteral">&quot; &quot;</span> &lt;&lt; it_map-&gt;first &lt;&lt; std::endl;</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;        std::vector&lt;bool&gt; good_indices_for_hypothesis (corresp_clusters.size (), <span class="keyword">true</span>);</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160; </div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;        <span class="keywordflow">if</span> (threshold_accept_model_hypothesis_ &lt; 1.f)</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;        {</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;          <span class="comment">//sort the hypotheses for each model according to their correspondences and take those that are threshold_accept_model_hypothesis_ over the max cardinality</span></div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;          <span class="keywordtype">int</span> max_cardinality = -1;</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; corresp_clusters.size (); i++)</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;          {</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;            <span class="comment">//std::cout &lt;&lt;  (corresp_clusters[i]).size() &lt;&lt; &quot; -- &quot; &lt;&lt; (*(*it_map).second.correspondences_to_inputcloud).size() &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;            <span class="keywordflow">if</span> (max_cardinality &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (corresp_clusters[i].size ()))</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;            {</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;              max_cardinality = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (corresp_clusters[i].size ());</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;            }</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;          }</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160; </div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;          <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; corresp_clusters.size (); i++)</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;          {</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;            <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> ((corresp_clusters[i]).size ()) &lt; (threshold_accept_model_hypothesis_ * <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (max_cardinality)))</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;            {</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;              good_indices_for_hypothesis[i] = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;            }</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;          }</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;        }</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160; </div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; corresp_clusters.size (); i++)</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        {</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160; </div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;          <span class="keywordflow">if</span> (!good_indices_for_hypothesis[i])</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;            <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160; </div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;          <span class="comment">//drawCorrespondences (processed, it_map-&gt;second, keypoints_pointcloud, corresp_clusters[i]);</span></div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160; </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;          Eigen::Matrix4f best_trans;</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;          <span class="keyword">typename</span> <a class="code" href="classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html">pcl::registration::TransformationEstimationSVD &lt; PointInT, PointInT &gt;</a> t_est;</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;          t_est.<a class="code" href="classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html#ab8e5a51823fded01fb75c2d082c5e49c">estimateRigidTransformation</a> (*(*it_map).second.correspondences_pointcloud, *keypoints_pointcloud, corresp_clusters[i], best_trans);</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160; </div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;          models_-&gt;push_back ((*it_map).second.model_);</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;          transforms_-&gt;push_back (best_trans);</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160; </div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;        }</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;      }</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;    }</div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160; </div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    std::cout &lt;&lt; <span class="stringliteral">&quot;Number of hypotheses:&quot;</span> &lt;&lt; models_-&gt;size() &lt;&lt; std::endl;</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160; </div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160;    <span class="keywordflow">if</span> (ICP_iterations_ &gt; 0)</div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;    {</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;      <a class="code" href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a> ticp (<span class="stringliteral">&quot;ICP &quot;</span>);</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160; </div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;      <span class="comment">//Prepare scene and model clouds for the pose refinement step</span></div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;      PointInTPtr cloud_voxelized_icp (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a> ());</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;      <a class="code" href="classpcl_1_1_voxel_grid.html">pcl::VoxelGrid&lt;PointInT&gt;</a> voxel_grid_icp;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;      voxel_grid_icp.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (processed);</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;      voxel_grid_icp.<a class="code" href="classpcl_1_1_voxel_grid.html#aa5d7831e665977bdce76ed05bd0005cf">setLeafSize</a> (VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_, VOXEL_SIZE_ICP_);</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;      voxel_grid_icp.<a class="code" href="classpcl_1_1_filter.html#a17115897ca28f6b12950d023958aa641">filter</a> (*cloud_voxelized_icp);</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;      <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a570b642caf7da50b71ce536fa65a51a6">source_</a>-&gt;voxelizeAllModels (VOXEL_SIZE_ICP_);</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160; </div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;<span class="preprocessor">#pragma omp parallel for schedule(dynamic,1) num_threads(omp_get_num_procs())</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; static_cast&lt;int&gt;(models_-&gt;size ()); i++)</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;      {</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160; </div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;        ConstPointInTPtr model_cloud;</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;        PointInTPtr model_aligned (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;        model_cloud = models_-&gt;at (i).getAssembled (VOXEL_SIZE_ICP_);</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;        <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a> (*model_cloud, *model_aligned, transforms_-&gt;at (i));</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160; </div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;        <span class="keyword">typename</span> pcl::registration::CorrespondenceRejectorSampleConsensus&lt;PointInT&gt;::Ptr rej (</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;            <span class="keyword">new</span> <a class="code" href="classpcl_1_1registration_1_1_correspondence_rejector_sample_consensus.html">pcl::registration::CorrespondenceRejectorSampleConsensus&lt;PointInT&gt;</a> ());</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160; </div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;        rej-&gt;setInputTarget (cloud_voxelized_icp);</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;        rej-&gt;setMaximumIterations (1000);</div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;        rej-&gt;setInlierThreshold (0.005f);</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;        rej-&gt;setInputSource (model_aligned);</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160; </div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;        <a class="code" href="classpcl_1_1_iterative_closest_point.html">pcl::IterativeClosestPoint&lt;PointInT, PointInT&gt;</a> reg;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;        reg.<a class="code" href="classpcl_1_1_registration.html#a663e64d6d5103eb937addd3e33104cf6">addCorrespondenceRejector</a> (rej);</div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;        reg.<a class="code" href="classpcl_1_1_iterative_closest_point.html#a8876f743cc31471a975e950e66c179d4">setInputTarget</a> (cloud_voxelized_icp); <span class="comment">//scene</span></div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;        reg.<a class="code" href="classpcl_1_1_iterative_closest_point.html#ad5215429e057c8760ac48e9bdb09b12c">setInputSource</a> (model_aligned); <span class="comment">//model</span></div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;        reg.<a class="code" href="classpcl_1_1_registration.html#a3844d186f7a99d15464368e0f25635ed">setMaximumIterations</a> (ICP_iterations_);</div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;        reg.<a class="code" href="classpcl_1_1_registration.html#a65596dcc3cb5d2647857226fb3d999a5">setMaxCorrespondenceDistance</a> (VOXEL_SIZE_ICP_ * 4.f);</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160; </div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;        <span class="keyword">typename</span> pcl::PointCloud&lt;PointInT&gt;::Ptr output_ (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a> ());</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;        reg.<a class="code" href="classpcl_1_1_registration.html#a96212303ca16b6d60020824086887c4f">align</a> (*output_);</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160; </div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;        Eigen::Matrix4f icp_trans = reg.<a class="code" href="classpcl_1_1_registration.html#a1e68bd39ac943131dcbf1431f9afe3f3">getFinalTransformation</a> ();</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;        transforms_-&gt;at (i) = icp_trans * transforms_-&gt;at (i);</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;      }</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;    }</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a>)</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    {</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160; </div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;      <a class="code" href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a> thv (<span class="stringliteral">&quot;HV verification&quot;</span>);</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160; </div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;      std::vector&lt;typename pcl::PointCloud&lt;PointInT&gt;::ConstPtr&gt; aligned_models;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;      aligned_models.resize (models_-&gt;size ());</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; models_-&gt;size (); i++)</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160;      {</div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;        ConstPointInTPtr model_cloud = models_-&gt;at (i).getAssembled (0.0025f);</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;        <span class="comment">//ConstPointInTPtr model_cloud = models_-&gt;at (i).getAssembled (VOXEL_SIZE_ICP_);</span></div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;        PointInTPtr model_aligned (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;PointInT&gt;</a>);</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;        <a class="code" href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a> (*model_cloud, *model_aligned, transforms_-&gt;at (i));</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        aligned_models[i] = model_aligned;</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;      }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160; </div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;      std::vector&lt;bool&gt; mask_hv;</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;      <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a>-&gt;setSceneCloud (<a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a7c4309d4d9e564900e9f230e3c4fdf04">input_</a>);</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;      <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a>-&gt;addModels (aligned_models, <span class="keyword">true</span>);</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;      <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a>-&gt;verify ();</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;      <a class="code" href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">hv_algorithm_</a>-&gt;getMask (mask_hv);</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160; </div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;      boost::shared_ptr &lt; std::vector&lt;ModelT&gt; &gt; models_temp;</div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;      boost::shared_ptr &lt; std::vector&lt;Eigen::Matrix4f, Eigen::aligned_allocator&lt;Eigen::Matrix4f&gt; &gt; &gt; transforms_temp;</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160; </div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;      models_temp.reset (<span class="keyword">new</span> std::vector&lt;ModelT&gt;);</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;      transforms_temp.reset (<span class="keyword">new</span> std::vector&lt;Eigen::Matrix4f, Eigen::aligned_allocator&lt;Eigen::Matrix4f&gt; &gt;);</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160; </div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; models_-&gt;size (); i++)</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;      {</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;        <span class="keywordflow">if</span> (!mask_hv[i])</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160; </div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;        models_temp-&gt;push_back (models_-&gt;at (i));</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;        transforms_temp-&gt;push_back (transforms_-&gt;at (i));</div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;      }</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160; </div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;      models_ = models_temp;</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;      transforms_ = transforms_temp;</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160; </div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;    }</div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;  }</div>
<div class="ttc" id="aclassflann_1_1_matrix_html"><div class="ttname"><a href="classflann_1_1_matrix.html">flann::Matrix</a></div><div class="ttdef"><b>Definition:</b> flann_search.h:51</div></div>
<div class="ttc" id="aclasspcl_1_1_filter_html_a17115897ca28f6b12950d023958aa641"><div class="ttname"><a href="classpcl_1_1_filter.html#a17115897ca28f6b12950d023958aa641">pcl::Filter::filter</a></div><div class="ttdeci">void filter(PointCloud &amp;output)</div><div class="ttdoc">Calls the filtering method and returns the filtered dataset in output.</div><div class="ttdef"><b>Definition:</b> filter.h:132</div></div>
<div class="ttc" id="aclasspcl_1_1_iterative_closest_point_html"><div class="ttname"><a href="classpcl_1_1_iterative_closest_point.html">pcl::IterativeClosestPoint</a></div><div class="ttdoc">IterativeClosestPoint provides a base implementation of the Iterative Closest Point algorithm....</div><div class="ttdef"><b>Definition:</b> icp.h:95</div></div>
<div class="ttc" id="aclasspcl_1_1_iterative_closest_point_html_a8876f743cc31471a975e950e66c179d4"><div class="ttname"><a href="classpcl_1_1_iterative_closest_point.html#a8876f743cc31471a975e950e66c179d4">pcl::IterativeClosestPoint::setInputTarget</a></div><div class="ttdeci">virtual void setInputTarget(const PointCloudTargetConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input target (e.g., the point cloud that we want to align to the target)</div><div class="ttdef"><b>Definition:</b> icp.h:212</div></div>
<div class="ttc" id="aclasspcl_1_1_iterative_closest_point_html_ad5215429e057c8760ac48e9bdb09b12c"><div class="ttname"><a href="classpcl_1_1_iterative_closest_point.html#ad5215429e057c8760ac48e9bdb09b12c">pcl::IterativeClosestPoint::setInputSource</a></div><div class="ttdeci">virtual void setInputSource(const PointCloudSourceConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input source (e.g., the point cloud that we want to align to the target)</div><div class="ttdef"><b>Definition:</b> icp.h:177</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt; FeatureT &gt;</a></div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a1e68bd39ac943131dcbf1431f9afe3f3"><div class="ttname"><a href="classpcl_1_1_registration.html#a1e68bd39ac943131dcbf1431f9afe3f3">pcl::Registration::getFinalTransformation</a></div><div class="ttdeci">Matrix4 getFinalTransformation()</div><div class="ttdoc">Get the final transformation matrix estimated by the registration method.</div><div class="ttdef"><b>Definition:</b> registration.h:275</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a3844d186f7a99d15464368e0f25635ed"><div class="ttname"><a href="classpcl_1_1_registration.html#a3844d186f7a99d15464368e0f25635ed">pcl::Registration::setMaximumIterations</a></div><div class="ttdeci">void setMaximumIterations(int nr_iterations)</div><div class="ttdoc">Set the maximum number of iterations the internal optimization should run for.</div><div class="ttdef"><b>Definition:</b> registration.h:285</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a65596dcc3cb5d2647857226fb3d999a5"><div class="ttname"><a href="classpcl_1_1_registration.html#a65596dcc3cb5d2647857226fb3d999a5">pcl::Registration::setMaxCorrespondenceDistance</a></div><div class="ttdeci">void setMaxCorrespondenceDistance(double distance_threshold)</div><div class="ttdoc">Set the maximum distance threshold between two correspondent points in source &lt;-&gt; target....</div><div class="ttdef"><b>Definition:</b> registration.h:321</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a663e64d6d5103eb937addd3e33104cf6"><div class="ttname"><a href="classpcl_1_1_registration.html#a663e64d6d5103eb937addd3e33104cf6">pcl::Registration::addCorrespondenceRejector</a></div><div class="ttdeci">void addCorrespondenceRejector(const CorrespondenceRejectorPtr &amp;rejector)</div><div class="ttdoc">Add a new correspondence rejector to the list</div><div class="ttdef"><b>Definition:</b> registration.h:449</div></div>
<div class="ttc" id="aclasspcl_1_1_registration_html_a96212303ca16b6d60020824086887c4f"><div class="ttname"><a href="classpcl_1_1_registration.html#a96212303ca16b6d60020824086887c4f">pcl::Registration::align</a></div><div class="ttdeci">void align(PointCloudSource &amp;output)</div><div class="ttdoc">Call the registration algorithm which estimates the transformation and returns the transformed source...</div><div class="ttdef"><b>Definition:</b> registration.hpp:169</div></div>
<div class="ttc" id="aclasspcl_1_1_scope_time_html"><div class="ttname"><a href="classpcl_1_1_scope_time.html">pcl::ScopeTime</a></div><div class="ttdoc">Class to measure the time spent in a scope</div><div class="ttdef"><b>Definition:</b> time.h:118</div></div>
<div class="ttc" id="aclasspcl_1_1_voxel_grid_html"><div class="ttname"><a href="classpcl_1_1_voxel_grid.html">pcl::VoxelGrid</a></div><div class="ttdoc">VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.</div><div class="ttdef"><b>Definition:</b> voxel_grid.h:179</div></div>
<div class="ttc" id="aclasspcl_1_1_voxel_grid_html_aa5d7831e665977bdce76ed05bd0005cf"><div class="ttname"><a href="classpcl_1_1_voxel_grid.html#aa5d7831e665977bdce76ed05bd0005cf">pcl::VoxelGrid::setLeafSize</a></div><div class="ttdeci">void setLeafSize(const Eigen::Vector4f &amp;leaf_size)</div><div class="ttdoc">Set the voxel grid leaf size.</div><div class="ttdef"><b>Definition:</b> voxel_grid.h:223</div></div>
<div class="ttc" id="aclasspcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_html_a33fc233406168aa31c6d762457129ec5"><div class="ttname"><a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a33fc233406168aa31c6d762457129ec5">pcl::rec_3d_framework::LocalRecognitionPipeline::hv_algorithm_</a></div><div class="ttdeci">boost::shared_ptr&lt; HypothesisVerification&lt; PointInT, PointInT &gt; &gt; hv_algorithm_</div><div class="ttdoc">Hypotheses verification algorithm</div><div class="ttdef"><b>Definition:</b> local_recognizer.h:65</div></div>
<div class="ttc" id="aclasspcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_html_a3f3bf44d5aaaeda4c328e4d58ccf44c1"><div class="ttname"><a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a3f3bf44d5aaaeda4c328e4d58ccf44c1">pcl::rec_3d_framework::LocalRecognitionPipeline::cg_algorithm_</a></div><div class="ttdeci">boost::shared_ptr&lt; CorrespondenceGrouping&lt; PointInT, PointInT &gt; &gt; cg_algorithm_</div><div class="ttdoc">Point-to-point correspondence grouping algorithm</div><div class="ttdef"><b>Definition:</b> local_recognizer.h:62</div></div>
<div class="ttc" id="aclasspcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_html_a570b642caf7da50b71ce536fa65a51a6"><div class="ttname"><a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a570b642caf7da50b71ce536fa65a51a6">pcl::rec_3d_framework::LocalRecognitionPipeline::source_</a></div><div class="ttdeci">boost::shared_ptr&lt; Source&lt; PointInT &gt; &gt; source_</div><div class="ttdoc">Model data source</div><div class="ttdef"><b>Definition:</b> local_recognizer.h:56</div></div>
<div class="ttc" id="aclasspcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_html_a7c4309d4d9e564900e9f230e3c4fdf04"><div class="ttname"><a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#a7c4309d4d9e564900e9f230e3c4fdf04">pcl::rec_3d_framework::LocalRecognitionPipeline::input_</a></div><div class="ttdeci">PointInTPtr input_</div><div class="ttdoc">Point cloud to be classified</div><div class="ttdef"><b>Definition:</b> local_recognizer.h:53</div></div>
<div class="ttc" id="aclasspcl_1_1rec__3d__framework_1_1_local_recognition_pipeline_html_abc0861c5e2a7866c3e5d32c8b068ad82"><div class="ttname"><a href="classpcl_1_1rec__3d__framework_1_1_local_recognition_pipeline.html#abc0861c5e2a7866c3e5d32c8b068ad82">pcl::rec_3d_framework::LocalRecognitionPipeline::estimator_</a></div><div class="ttdeci">boost::shared_ptr&lt; LocalEstimator&lt; PointInT, FeatureT &gt; &gt; estimator_</div><div class="ttdoc">Computes a feature</div><div class="ttdef"><b>Definition:</b> local_recognizer.h:59</div></div>
<div class="ttc" id="aclasspcl_1_1registration_1_1_correspondence_rejector_sample_consensus_html"><div class="ttname"><a href="classpcl_1_1registration_1_1_correspondence_rejector_sample_consensus.html">pcl::registration::CorrespondenceRejectorSampleConsensus</a></div><div class="ttdoc">CorrespondenceRejectorSampleConsensus implements a correspondence rejection using Random Sample Conse...</div><div class="ttdef"><b>Definition:</b> correspondence_rejection_sample_consensus.h:60</div></div>
<div class="ttc" id="aclasspcl_1_1registration_1_1_transformation_estimation_s_v_d_html"><div class="ttname"><a href="classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html">pcl::registration::TransformationEstimationSVD</a></div><div class="ttdef"><b>Definition:</b> transformation_estimation_svd.h:59</div></div>
<div class="ttc" id="aclasspcl_1_1registration_1_1_transformation_estimation_s_v_d_html_ab8e5a51823fded01fb75c2d082c5e49c"><div class="ttname"><a href="classpcl_1_1registration_1_1_transformation_estimation_s_v_d.html#ab8e5a51823fded01fb75c2d082c5e49c">pcl::registration::TransformationEstimationSVD::estimateRigidTransformation</a></div><div class="ttdeci">void estimateRigidTransformation(const pcl::PointCloud&lt; PointSource &gt; &amp;cloud_src, const pcl::PointCloud&lt; PointTarget &gt; &amp;cloud_tgt, Matrix4 &amp;transformation_matrix) const</div><div class="ttdoc">Estimate a rigid rotation transformation between a source and a target point cloud using SVD.</div><div class="ttdef"><b>Definition:</b> transformation_estimation_svd.hpp:47</div></div>
<div class="ttc" id="agroup__common_html_ga52d532f7f2b4d7bba78d13701d3a33d8"><div class="ttname"><a href="group__common.html#ga52d532f7f2b4d7bba78d13701d3a33d8">pcl::transformPointCloud</a></div><div class="ttdeci">void transformPointCloud(const pcl::PointCloud&lt; PointT &gt; &amp;cloud_in, pcl::PointCloud&lt; PointT &gt; &amp;cloud_out, const Eigen::Transform&lt; Scalar, 3, Eigen::Affine &gt; &amp;transform, bool copy_all_fields=true)</div><div class="ttdoc">Apply an affine transform defined by an Eigen Transform</div><div class="ttdef"><b>Definition:</b> transforms.hpp:42</div></div>
<div class="ttc" id="agroup__common_html_gaa65b1c8d782e7b776ae682679d2d948f"><div class="ttname"><a href="group__common.html#gaa65b1c8d782e7b776ae682679d2d948f">pcl::copyPointCloud</a></div><div class="ttdeci">PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &amp;cloud_in, const std::vector&lt; int &gt; &amp;indices, pcl::PCLPointCloud2 &amp;cloud_out)</div><div class="ttdoc">Extract the indices of a given point cloud as a new point cloud</div></div>
<div class="ttc" id="astructpcl_1_1_correspondence_html"><div class="ttname"><a href="structpcl_1_1_correspondence.html">pcl::Correspondence</a></div><div class="ttdoc">Correspondence represents a match between two entities (e.g., points, descriptors,...</div><div class="ttdef"><b>Definition:</b> correspondence.h:59</div></div>
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